1. No category

Colak et al. Molecular Cancer 2010, 9:146
http://www.molecular-cancer.com/content/9/1/146
Open Access
RESEARCH
Integrative and comparative genomics analysis of
early hepatocellular carcinoma differentiated from
liver regeneration in young and old
Research
Dilek Colak*†1, Muhammad A Chishti†2, Al-Bandary Al-Bakheet3, Ahmed Al-Qahtani4,5, Mohamed M Shoukri1,
Malcolm H Goyns6, Pinar T Ozand7, John Quackenbush8, Ben H Park9 and Namik Kaya*3
Abstract
Background: Hepatocellular carcinoma (HCC) is the third-leading cause of cancer-related deaths worldwide. It is often
diagnosed at an advanced stage, and hence typically has a poor prognosis. To identify distinct molecular mechanisms
for early HCC we developed a rat model of liver regeneration post-hepatectomy, as well as liver cells undergoing
malignant transformation and compared them to normal liver using a microarray approach. Subsequently, we
performed cross-species comparative analysis coupled with copy number alterations (CNA) of independent early
human HCC microarray studies to facilitate the identification of critical regulatory modules conserved across species.
Results: We identified 35 signature genes conserved across species, and shared among different types of early human
HCCs. Over 70% of signature genes were cancer-related, and more than 50% of the conserved genes were mapped to
human genomic CNA regions. Functional annotation revealed genes already implicated in HCC, as well as novel genes
which were not previously reported in liver tumors. A subset of differentially expressed genes was validated using
quantitative RT-PCR. Concordance was also confirmed for a significant number of genes and pathways in five
independent validation microarray datasets. Our results indicated alterations in a number of cancer related pathways,
including p53, p38 MAPK, ERK/MAPK, PI3K/AKT, and TGF-β signaling pathways, and potential critical regulatory role of
MYC, ERBB2, HNF4A, and SMAD3 for early HCC transformation.
Conclusions: The integrative analysis of transcriptional deregulation, genomic CNA and comparative cross species
analysis brings new insights into the molecular profile of early hepatoma formation. This approach may lead to robust
biomarkers for the detection of early human HCC.
Background
Hepatocellular carcinoma (HCC) is the fifth most common cancer type, and is the third leading cause of cancer
mortality worldwide [1,2]. Recent reports show that HCC
is becoming more wide-spread and has dramatically
increased in North America Western Europe and Japan
[2-4]. Additionally, there is an increasing incidence of the
disease among younger age groups that warrants further
investigation [5,6].
* Correspondence: dcolakkaya@kfshrc.edu.sa, nkaya@kfshrc.edu.sa
1
Department of Biostatistics, Epidemiology and Scientific Computing, King
Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
3 Department of Genetics, King Faisal Specialist Hospital and Research Centre,
Riyadh, Saudi Arabia
† Contributed equally
Recently considerable attention has been placed on
global gene expression studies as well as genomic aberrations in order to understand the pathogenesis of HCC,
and to look for possible early markers of detection [7-13].
Although notable successes have been achieved, there
still exist significant challenges due to the heterogeneous
nature of HCC (and other cancers) as well as the complexity of the molecular pathogenesis of this disease.
Depending on etiological and accompanying pathological
conditions, such as viral infection, cirrhosis, inflammation, fibrosis and others, the HCC signature genes identified thus far vary considerably. Additionally, the study of
tumor formation in the liver is difficult due to the continuous transcriptome changes that occur during regeneration after hepatectomy [14], as well as age related gene
Full list of author information is available at the end of the article
© 2010 Colak et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.
Colak et al. Molecular Cancer 2010, 9:146
http://www.molecular-cancer.com/content/9/1/146
expression changes [15-17]. Similarly, cancer progresses
through a series of histopathological stages during which
genetic alterations accumulate, and a natural consequence of this are the dynamic changes in gene expression patterns that occur during hepatocellular
carcinogenesis. Developing animal models of HCC provide an experimental ground for dissecting the genetic
and biological complexities of human cancer and contribute to our ability to identify and characterize pathogenic
modifications relevant to early stages of cancer development and progression [18,19]. The previous studies have
used cross-species comparative genomics approach successfully to understand the molecular pathogenesis of
various cancers [20-23]. Hence, combining cross-species
comparative and/or functional genomics approaches with
independent datasets from human and animal models of
HCC along with genomic DNA copy number alterations
enhances the ability to identify robust predictive markers
for HCC [23-26].
Here we present a comparative and integrative functional genomics approach to find an early marker for
HCC. We developed a rat model and analyzed the transcriptomes of early HCC versus regenerated liver and
normal liver in both young and old age animals using a
microarray of more than 27,000 annotated genes from
Celera and public repositories. We then performed crossspecies comparative genomics analysis to identify genes
that are conserved in rat and human early HCCs by reanalyzing independent datasets for human early HCC
microarray expression profiling data [8,27] and also comparing with the Stanford HCC microarray data [7].
Finally, we performed an integrative analysis of DNA
genomic copy number alterations (CNAs) and gene
expression profiles (schematically outlined in Figure 1).
Our findings include some genes already reported to be
associated with human HCC, thus validating our
approach. We also report many other novel genes which
were not reported previously in liver cancers. Furthermore, we validated the high expression of eight potential
biomarker genes from the blood of patients with early
HCC using realtime RT-PCR. Our comparative and integrative genomics approach involving the integration of
multiple high dimensional independent datasets may lead
to robust biomarkers for the detection of early HCC.
Results
Gene Expression Profiling Confirms Pathological
Classification
We performed genome-wide gene expression profiling of
24 samples for early HCC, regenerated liver and normal
liver of both young and old rats using Applied Biosystems
Rat Genome Survey microarray which includes more
than 27,000 annotated genes from Celera and public
repositories. To find genes that were differentially
Page 2 of 19
expressed across three different "treatment" types (i.e.
early HCC, regenerated, and normal), and two age groups
(old and young), we performed two-factor ANOVA to
look for variations due to treatment, age and their interactions. The ANOVA identified 432 and 4063 genes that
were significantly modulated by treatment type and age
with p < 0.01, respectively. In addition, we found 322
genes that showed a significant interaction of age and
treatment effect (data not shown). The unsupervised
two-dimensional hierarchical clustering as well as principal component analysis (PCA) using genes which varied
significantly with the treatment effect clustered samples
according to their treatment type for both old and young
(Figure 2A and 2B), hence supporting the conclusion that
gene expression profiles robustly reflected the histological classification.
Identifying HCC Specific Genes Conserved Across Old and
Young
The ANOVA identified 432 genes that showed significant
expression differences due to treatment in both age
groups (p-value < 0.01) were subjected to a template
matching algorithm (TMA) [28] to identify HCC specific
genes conserved across both age groups. We identified 96
up-regulated and 38 down-regulated genes specific to
HCC (template-match p-values < 0.01) (Figure 2C and
Additional file 1).
The gene ontology and functional network analyses of
HCC specific genes were performed using the Ingenuity
knowledge base. The biological functions assigned to the
dataset are ranked according to the significance of that
biological function to the dataset. The enriched functional categories and diseases include carcinogenesis, cell
cycle, immune response, cell morphology, cellular development, and growth and proliferation (Figure 2D). The
PANTHER also revealed that signal transduction (pvalue = 7.17 × 10-4), proteolysis (p-value = 1.59 × 10-3),
cell motility (p-value = 3.91 × 10-3), immunity and
defense (p-value = 1.07 × 10-2), and cell proliferation and
differentiation (p-value = 4.9 × 10-2) were among the
most enriched biological processes in the HCC specific
genes. The most significantly altered pathways include
p53, p38 MAPK, insulin/IGF pathway-protein kinase B
signaling cascade, apoptosis, interleukin and integrin signaling pathways. The gene interaction network also corroborated with the altered pathways (Figure 2E).
Age-Dependent Differences in Early HCC Differentiated
from Regeneration
We found a significant interaction of age and treatment
effect in our two-factor ANOVA analysis. Indeed, the
hepatic transcriptome changes with ageing as in other
cancer types, and age is a potential confounding factor
embedded in gene expression profiles [15,16]. Therefore,
Colak et al. Molecular Cancer 2010, 9:146
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Page 3 of 19
Figure 1 Integrative and cross-species comparative genomics approach to identify evolutionary conserved inter-species biomarkers for
early HCC differentiated from liver regeneration. Gene expression signature for early rat HCC is differentiated from liver regeneration and normal
liver in young and old using a microarray approach. Next, the cross-species comparative analysis was performed to identify genes that are conserved
in early rat HCCs and in multiple independent early human HCCs, which would facilitate the identification of critical regulatory modules in the expression profiles. Finally, the integrative analysis of genomic copy number alteration (CNA) regions and gene expression profiles as well as independent
validation analyses both in silico and with quantitative realtime RT-PCR were performed. HCC, hepatocellular carcinoma.
we stratified samples as young and old cohorts, and identified HCC and regeneration specific genes using oneway ANOVA in each age group separately. The ANOVA
identified 925 and 408 significantly dysregulated genes
(up or down) due to the different treatment types in old
and young animal groups (p-value < 0.02), respectively.
The hierarchical clustering in both dimensions (samples
and genes), as well as the PCA clearly separated samples
based on the treatment type (Additional file 2), The gene
expression clustering distance between the HCC group
and the other two groups (regenerated and normal) was
the greatest in both age groups (Additional file 2).
Early HCC signature genes in each age group were
obtained by overlapping gene lists. Each circle in the
Venn diagram represents the differential expression
between two treatment types. We identified 80 genes and
100 genes specific to hepatoma in young and old, respectively (Figure 3A and Additional file 3). As seen from the
heatmap of HCC specific genes, these sets of genes were
exclusively up/down regulated in the HCC group only
(Figure 3B and Additional file 3). The expression of Pbsn,
Lum, Adam8, Ctse, Calb3, Fbn1, Agtpbp1, Prom1, Ela1,
Tnfsf13, and Ap2b1 were significantly altered in both
young and old rats with early HCC. The genes A2m,
Cdh13, Mas1, Slack, Cidea, and Dcn were significantly
dysregulated exclusively in HCC in the young; whereas
Cxcl5, Lox, Slc25a2, Rmt1, and Nid2, were specific to
HCC in old rats. We also identified regeneration specific
genes in old and young rats in a similar approach (data
not shown).
Colak et al. Molecular Cancer 2010, 9:146
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Page 4 of 19
Figure 2 Early HCC signature genes conserved across old and young rats. (A) The unsupervised two-dimensional hierarchical clustering using
genes that were significantly modulated due to treatment type across all samples (p < 0.01) clustered samples based on their treatment groups (HCC,
regenerated and normal). Highly expressed genes are indicated in red, intermediate in black and weakly expressed in green. (B)The three dominant
PCA components that contained around 60% of the variance in the data matrix separated samples based on treatment as well as age groups. (C) Heatmap of HCC signature genes conserved across old and young (D) Functional analysis of HCC specific genes. X-axis indicates the significance (-log pvalue) of the functional association that is dependent on the number of genes in a class as well as biologic relevance (E) Gene interaction network of
HCC specific genes generated by IPA analysis. Nodes represent genes, with their shape representing the functional class of the gene product, and
edges indicate biological relationship between the nodes.
Colak et al. Molecular Cancer 2010, 9:146
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Page 5 of 19
Figure 3 Heatmap and gene interaction networks of early HCC specific genes in young. (A) Venn diagram characterizing differential gene expression between and specific to different treatment types: early rat HCC (DY), regenerated (RY), and normal (NY). The number of HCC specific genes,
80, is circled in black. (B) Heatmap of HCC specific genes exclusively dysregulated (up/down regulated) in the HCC group only. (C-E) Top three scoring
gene interaction networks (with highest relevance scores). Nodes represent genes, with their shape representing the functional class of the gene
product, and edges indicate biological relationship between the nodes (see legend in Figure 2). (F) Top network functions associated with three networks shown. An IPA score of three indicates that there is 1/1000 (score = -log (p-value)) chance that the focus genes are assigned to a network randomly.
Colak et al. Molecular Cancer 2010, 9:146
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Page 6 of 19
Functional Comparison of Hepatoma and Regeneration in
Young and Old
Integrative Analysis of Transcriptional Deregulation with
Genomic Copy Number Aberrations
Early HCC signature genes in young animals were mainly
associated with cancer, cell cycle, immune response, cellular function and maintenance, development, cell adhesion-mediated signaling, proteolysis, and signal
transduction, whereas genes in old animals were highly
associated with cancer, cellular movement, extracellular
transport and import, cell adhesion, tissue development,
cell morphology, cell-to-cell signaling and interaction. On
the other hand, the regeneration specific genes were
mainly associated with mRNA transcription and regulation, lipid metabolism, protein modification, protein
phosphorylation, cell morphology, cellular development,
small molecule biochemistry, and cellular growth and
proliferation (Table 1). To gain more insights into HCC
pathogenesis for young and old age early HCCs, we carried out gene interaction networks of HCC specific genes
in young and old (Figure 3C, D and 3E and Additional file
3, respectively). The interaction networks highlight the
important role of p53, p38 MAPK, ERK/MAPK, PI3K/
AKT signaling, NF-κB and TGF-β pathways in early rat
HCC.
Various studies have reported chromosomal instability at
chromosomal regions associated with many cancers,
including human HCC copy number (CN) status
[7,9,26,29,30]. These genomic modifications, which in
part are reflected in changes in DNA copy number (CN),
may alter the transcriptional control mechanism, and
hence impact gene expression levels [31,32]. Hence, we
compiled genes located in CNA regions reported in three
independent genome copy number studies of human
HCC [7,9,10]. The integration of 154 early HCC signature
genes with the copy number data resulted in 75 genes
that mapped to human CNA chromosomal loci genes
including COL1A1, CCNA2, NFATC2, F2, DCK, MMP2,
GJA1, VIM, LGALS3BP, and SP100. The interaction network of those genes further corroborated with the activation of the NF-κB, p38 MAPK, AP1, and JNK pathways
(Figure 5). We found that almost 50% of the 35 cross-species conserved signature genes that are common to different types of early HCCs analyzed were mapped to
genomic locations within the CNA regions (Table 2).
Cross-Species Comparative Genomic Analysis
As a validation of our results, we analyzed four independently performed microarray datasets for early human
and rat HCCs [19,33-35] using the analysis procedure
defined in the "Methods" section on the new datasets.
The first validation dataset was from Chiang et.al. [33]
using Affymetrix short oligo arrays. The dataset was
composed of 91 HCV-related HCC tumor samples, of
which 65 were very early or early stage disease, which we
used in our re-analysis and comparison. The re-analyzed
validation dataset showed a significant number of genes
(p < 10-5) in common with our analysis results. In fact,
more than 50% of our cross-species conserved genes were
also differentially expressed in early HCC compared to
normal controls in the validation dataset (Table 2). The
significance of overlaps was calculated using hypergeometric distributional assumption [36] and p-values were
adjusted using Bonferroni correction for multiple comparisons [37]. In addition, unsupervised clustering was
performed using our 35- gene signature to cluster the
samples from Chiang et. al. We found that using our signature gene list was sufficient to separate individuals in
Chiang et. al.'s study as either early HCC patients or normal controls (Additional file 5).
Moreover, we found a significant number of genes in
common with the second dataset from Boyault et. al. [34]
which consisted of 57 human HCCs and five samples of
pooled non-tumorus tissues, and a third gene expression
dataset from Liao et. al. [35] consisting of human HCC
from various stages (we used expression data for only the
early stage of the disease) (Table 2). Furthermore, we
To identify how many of our early rat HCC signature
genes were conserved in early human HCCs, we re-analyzed two independently performed microarray datasets
for early human HCC from Wurmbach et. al. [8] and Mas
et. al. [27]. The Wurmbach et. al.'s dataset composed of
19 early HCC patients and 10 normal controls, and the
Mas et. al. dataset was composed of 16 cirrhotic livers
with early HCC, 38 HCV associated HCC, and 19 normal
livers. In addition, we also compared our signature genes
with the OncodB.HCC database for 57 HCC patients
from the Stanford HCC microarray data. The comparison
of our rat early HCC signature genes (human orthologous) with the re-analyzed early human HCCs datasets
revealed that 154 unique genes were conserved with early
human HCCs (p < 0.001), and 35 of those were shared by
all datasets analyzed (Table 2, Figure 1). We found that
unsupervised clustering using the conserved signature
gene list across species was sufficient to separate individuals in both Wurmbach's and Mas' human HCC samples
as either early HCC patients or normal controls (data not
shown). The gene interaction network analysis of the 154
signature genes indicated the importance of NF-κB, RAS
and JNK activation in early hepatoma formation (Additional file 4). The network analysis of 35 cross-species
conserved early HCC signature genes reveals the important roles of ERK/MAPK, PI3K/AKT, and TGF-β pathways (Figure 4). It also indicated a potential critical
regulatory role of MYC, ERBB2, HNF4A, and SMAD3 for
malignant transformation to early HCC (Figure 4B).
Independent Validation Set Analysis
Colak et al. Molecular Cancer 2010, 9:146
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Table 1: Functional comparison of hepatoma and regeneration in young and old
Molecular and Cellular Functions
Significance
Number of genes1
(%)
Hepatoma in Young
2.1 × 10-3 - 4.9 × 10-2
12
33.3
Cellular Development
4.6 ×
× 10-2
8
22.2
Immune Response
2.5 × 10-5 - 4.5 × 10-2
7
19.4
Skeletal and Muscular System Development and Function
9.1 ×
× 10-2
7
19.4
Cell Morphology
4.6 × 10-3 - 4.9 × 10-2
7
19.4
Nervous System Development and Function
4.6 ×
× 10-2
6
16.7
Hair and Skin Development and Function
2.1 × 10-5 - 1.4 × 10-2
4
11.1
Cell Cycle
8.6 ×
× 10-2
4
11.1
Cellular Function and Maintenance
2.1 × 10-4 - 4.5 × 10-2
4
11.1
Amino Acid Metabolism
4.6 ×
× 10-2
3
8.3
Cell Death
4.6 × 10-3 - 4.9 × 10-2
3
8.3
3.6 × 10-3 - 4.8 × 10-2
15
46.9
13
40.6
10
31.3
Tissue Development
4.7 ×
× 10-2
9
28.1
Cell Morphology
4.9 × 10-3 - 4.9 × 10-2
9
28.1
Organ Development
3.1 ×
× 10-2
6
18.8
Embryonic Development
3.4 × 10-3 - 4.7 × 10-2
5
15.6
Organ Morphology
3.1 ×
× 10-2
4
12.5
Cell Death
4.9 × 10-3 - 3.9 × 10-2
4
12.5
Skeletal and Muscular System Development and Function
4.7 ×
× 10-2
3
9.4
Amino Acid Metabolism
4.9 × 10-3 - 4.3 × 10-2
2
6.3
1.6 × 10-3 - 4.8 × 10-2
Cancer
10-3 -
10-4 -
10-3 -
10-5 -
10-3 -
4.9
4.5
4.9
4.8
4.9
Hepatoma in Old
Cancer
10-3 -
4.8
× 10-2
Cellular Movement
2.3 ×
Cell-to-Cell Signaling and Interaction
4.9 × 10-3 - 4.8 10-2
10-3 -
10-3 -
10-3 -
10-3 -
3.9
4.8
3.4
1.9
Regenerated in Young
5
38.5
Skeletal and Muscular System Development and Function
1.6 ×
× 10-2
5
38.5
Cell Morphology
1.6 × 10-3 - 4.9 × 10-2
4
30.8
Cellular Assembly and Organization
1.6 ×
× 10-2
4
30.8
Cell-to-Cell Signaling and Interaction
1.6 × 10-3 - 4.9 × 10-2
4
30.8
Small Molecule Biochemistry
1.6 ×
× 10-2
4
30.8
Tissue Development
1.9 × 10-3 - 4.8 × 10-2
4
30.8
Cellular Development
1.6 ×
× 10-2
3
23.1
Cell cycle
1.4 × 10-3 - 5.0 × 10-2
3
23.1
Amino Acid Metabolism
1.6 ×
× 10-2
2
15.4
Cellular Function and Maintenance
1.6 × 10-3 - 3.2 × 10-2
2
15.4
× 10-2
2
15.4
Nervous System Development and Function
4.2 × 10-5 - 4.2 × 10-2
22
37.9
Molecular Transport
3.8 × 10-3 - 4.2 × 10-2
19
32.8
Cell Morphology
1.3 ×
× 10-2
15
25.9
Small Molecule Biochemistry
1.1 × 10-3 - 4.2 × 10-2
15
25.9
15
25.9
Cellular Growth and Proliferation
Nervous System Development and Function
1.6 ×
10-3 -
10-3 -
10-3 -
10-3 -
10-3 -
10-3 -
3.7
4.9
4.7
4.9
4.6
4.8
Regenerated in Old
Cellular Movement
1.5 ×
10-4 -
10-3 -
3.8
4.2
× 10-2
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Table 1: Functional comparison of hepatoma and regeneration in young and old (Continued)
Cell Signaling
7.1 × 10-3 - 2.3 × 10-2
15
25.9
Cell-to-Cell Signaling and Interaction
7.1 ×
× 10-2
12
20.7
Tissue Morphology
2.3 × 10-4 - 4.2 × 10-2
11
19.0
Lipid Metabolism
1.1 ×
× 10-2
10
17.2
Cellular Growth and Proliferation
3.2 × 10-3 - 3.7 × 10-2
10
17.2
Skeletal and Muscular System Development and Function
3.2 ×
× 10-2
9
15.5
Cellular Assembly and Organization
7.1 × 10-3 - 4.1 × 10-2
8
13.8
Cellular Function and Maintenance
7.1 ×
× 10-2
7
12.1
Cellular Development
1.9 × 10-4 - 3.7 × 10-2
5
8.6
10-3 -
10-3 -
10-3 -
10-3 -
3.9
3.8
3.7
3.5
1Thirty six, 32, 13, and 94 genes of the signature genes for HCC in young, HCC in old, Regenerated in young and Regenerated in old, respectively,
mapped to corresponding genes in the knowledgebase.
obtained consistent results with the DEN-induced HCC
in rats from [19,38]. The IPA functional and network
analysis of all validation datasets revealed a significant
number of overrepresented functional categories and
pathways in common with our results. Of note, cell death,
cancer, cellular development, cellular growth and proliferation, organismal development, transport, and cell
cycle came up as significantly enriched categories in both
the validation datasets and our analyses. The interaction
networks analyses of significantly dysregulated genes in
validation datasets highlight the important roles of MYC,
ERK/MAPK, AKT, NF-κB and TGF-β signaling pathways. The similar findings between our results and the
independent validation sets argue against random chance
accounting for the observed enrichment of these functional categories and pathways.
Validation of Microarray Data for Early Rat HCC by Realtime
RT-PCR
To confirm the microarray results by an independent
method, we validated expression levels of six randomly
selected differentially regulated genes (Pbsn, Cdh13, Lum,
Nid2, Dcn, Slc22a5) in early rat HCC by realtime quantitative RT-PCR. A highly significant correlation existed
between the microarray and realtime RT-PCR results (r =
0.97, p value < 0.001) (Figure 6), thus demonstrating the
reliability of our gene expression measurements. The
selected genes and their interaction networks with other
Figure 4 The gene interaction networks of early HCC potential biomarker genes that are conserved in rat early HCCs and in multiple independent human early HCCs. The network analysis of 35 early HCC signature genes indicated the activation of ERK/MAPK, PI3K/AKT and TGF-β signaling pathways, as well as potential critical regulatory roles of MYC, ERbB-2, HNF4A, and SMAD3 for early HCC; top two scoring networks are shown (A,
B).
Colak et al. Molecular Cancer 2010, 9:146
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genes are shown (Figures 3C, D and 3E, 4A, and Additional file 3).
Validation of Potential Biomarker Genes from Whole Blood
of Patients with Early HCC Using qRT-PCR
To further validate the differential expression of potential
biomarker genes using realtime RT-PCR from the blood
of early HCC patients and healthy control subjects (ten
subjects in each group), we selected eight genes (GJA1,
VIM, IGFBP3, COL1A1, SP100, MMP2, LGALS3BP, and
DPP4) among early HCC gene signature with CNA
(denoted with asterisk in Table 2) and were differentially
regulated in at least one of the independent datasets. We
confirmed a statistically significant increase in the
expression of these biomarker genes in early HCC
patients relative to healthy control subjects (p-value <
0.05) (Table 2, Figure 7); hence demonstrating the robustness of the cross-species integrated genomics procedure.
Discussion
The present study sought to identify evolutionarily conserved inter-species biomarkers for early HCC differentiated from liver regeneration using integrative and crossspecies comparative genomic approaches. The main contributions of this study are as follows: First, we developed
a rat model of liver regeneration post-hepatectomy
(return to quiescence), as well as liver cells undergoing
malignant transformation and compared them to normal
liver using a comprehensive microarray of 27,000 publicly
available and Celera annotated rat genes. We included the
liver regeneration in our model, as regeneration is a critical component in the surgical treatment of HCC, and frequently associated with HCC occurrence [39]. Though
liver cells can regenerate, they do not typically transform
and lead to HCC [40]. Therefore, an early HCC marker
needs to be unique for the tumorigenic process and not
overlap with the transcriptome changes that occur in
regenerating or normal liver tissue. As ageing is also
known to be a confounding factor embedded in gene
expression profile data [15-17], we included age as a factor in our multi-factor statistical analysis, and identified
age-specific differences in early HCC. Secondly, we performed cross-species comparative analysis to identify
genes that are conserved in early rat HCCs and in multiple independently performed early human HCCs which
would facilitate the identification of critical regulatory
modules conserved across species in the expression profiles. Finally, we integrated genomic CNA data associated
with the human HCCs with the transcriptomic profile,
and performed validation analyses both in silico and with
quantitative realtime RT-PCR (as schematically outlined
in Figure 1). As CNAs have clear impact on expression
levels in a variety of tumors [26,29,30], this dual strategy
is very effective for interpreting the DNA and RNA level
Page 9 of 19
anomalies in cancer, in order to identify genes involved
with tumor initiation and progression [24,26].
The validation analyses demonstrated great concordance of our results with other data sets using various
microarray platforms. The ABI 1700 system has a unique
approach in identifying dysregulated genes since it targets genes from both Celera and Public databases and utilizes chemiluminescently enhanced detection that is
likely to determine relatively rare mRNAs. Also, our confirmatory quantitative realtime RT-PCR experiments displayed a strong correlation with the microarray results,
adding to the validity of the present observations. This is
in agreement with some recent studies showing a linear
relationship for real-time and conventional reverse transcription and therefore validates the robustness of mRNA
quantification using either microarrays or quantitative
RT-PCR[41]. Hence this allowed us to identify potential
biomarkers for human early HCC and to gain further
insight into the mechanism of early hepatoma formation.
We performed a two-step algorithm to identify early rat
HCC signature genes: In the first step, a two-way
ANOVA was performed including treatment and age as
well as their interactions into our statistical model and we
identified genes uniquely expressed in early HCC in both
young and old rats (Figure 2). In the second step, because
the interaction between age and treatment was significant, we stratified our samples as young and old cohorts,
and HCC specific genes were identified using two oneway ANOVA in each age group separately [42]. Finally,
the HCC specific genes in both young and old identified
in the two steps were combined before performing the
cross-species comparison (as detailed in "Material and
Methods" section, and schematically shown in Figure 1).
When comparing the early HCC group with the regeneration or normal cohorts to identify the differentially
expressed genes we used a set of criteria: S/N ratio > 3 in
> 50% of the samples, a p-value < 0.02 and absolute fold
change > 1.8. These observations are consistent with the
study of Guo et. al., in which gene lists ranked by fold
change and filtered with non-stringent statistically significant tests were more reproducible across platforms than
those generated through other analytical procedures
[43,44]. In addition, as Ghosh et. al. discuss on combining
data from multiple gene expression studies, if two studies
independently discover that the same gene/protein to be
differentially expressed, then the chance of error is significantly reduced [45].
The comparison of our signature genes with three different independently performed early human HCC
microarray data sets revealed a significant number of
early rat HCC genes (human orthologous) conserved
across early human HCCs (p < 0.001). Indeed, many of
those genes were related to cancer. For example, LUM,
CCNA2, IGFBP3, HPX, COL1A1, SRPX, VIM, TGFBR1,
Page 10 of 19
Table 2: List of 35 cross-species conserved early HCC signature genes with qRT-PCR and independent human/rat early HCC validations overlaid
Gene Symbol1
Description
Chromosomal Loci
P-value
qRT-PCR
Independent Validation Datasets.
Human
Colak et al. Molecular Cancer 2010, 9:146
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VIM*
vimentin
DCN
decorin
EIF4EBP1*
eukaryotic translation initiation factor 4E binding protein 1
AQP1
aquaporin 1 (channel-forming integral protein, 28 kDa)
IMPA2
inositol(myo)-1(or 4)-monophosphatase 2
FBN1
fibrillin 1
DCK*
deoxycytidine kinase
GJA1*
gap junction protein, alpha 1,43kDa (connexin 43)
SP100*
SP100 nuclear antigen
Rat
Chiang et al.
Boyault et al.
Liao et al.
Liu et al.
1.49E-16
DEa
NS
NS
NS
DEc
12q21.33
2.86E-15
DEb
DEc
DEc
DEc
DEc
8p12
8.91E-15
NA
DEc
NS
NS
NS
NS
NS
NS
NS
10p13
7p14
1.15E-14
NA
NS
DEd
18p11.2
7.74E-14
NA
NS
DEd
NS
15q21.1
1.75E-12
NA
NS
4q13.3-q21.1
4.22E-11
NA
DEc
NS
NS
DEc
NS
6q21-q23.2
5.08E-11
DEa
NS
DEc
DEd
DEc
2q37.1
6.95E-11
DEa
DEc
NS
NS
NS
SGCB*
sarcoglycan, beta (43kDa dystrophin-associated glycoprotein)
4q12
3.18E-10
NA
DEc
NS
NS
NS
FGFR2*
fibroblast growth factor receptor 2
10q26
1.15E-09
NA
NS
NS
DEd
NS
ABCC9
ATP-binding cassette, sub-family C (CFTR/MRP), member 9
12p12.1
1.75E-09
NA
DEc
NS
DEc
NS
MMP2*
matrix metallopeptidase 2
16q13-q21
1.22E-08
DEa
DEd
DEc
NS
NS
DEd
NS
NS
DEc
LGALS3BP*
lectin, galactoside-binding, soluble, 3 binding protein
17q25
4.29E-08
DEa
GSTA1*
glutathione S-transferase A1
6p12.1
5.16E-08
NA
NS
NS
NS
NS
DEd
DEd
NS
DEd
NS
NAT8
N-acetyltransferase 8
HMGB2*
high-mobility group box 2
2p13.1-p12
3.20E-07
NA
DEc
4q31
3.34E-07
NA
DEc
DEd
11q22.3
6.15E-07
NA
DEc
NS
NS
NS
17q21.3-q22.1
7.62E-07
DEa
DEd
NS
NS
DEc
polymerase (RNA) mitochondrial (DNA directed)
19p13.3
7.95E-07
NA
DEc
NS
DEc
NS
AGPAT2
1-acylglycerol-3-phosphate O-acyltransferase 2
(lysophosphatidic acid acyltransferase, beta)
9q34.3
1.04E-06
NA
DEc
DEc
DEd
NS
CTBP2*
C-terminal binding protein 2
10q26.13
1.09E-06
NA
NS
NS
DEd
NS
DEd
NS
NS
DEc
NS
NS
NS
DEc
DEd
NS
NS
NS
DEd
DEc
EXPH5
exophilin 5
COL1A1*
collagen, type I, alpha 1
POLRMT
PROM1
prominin 1
4p15.32
2.49E-06
NA
DEc
HPX*
hemopexin
11p15.5-p15.4
3.00E-06
NA
DEc
HEPH
hephaestin
Xq11-q12
3.15E-06
NA
NS
HLA-DQA1*
major histocompatibility complex, class II, DQ alpha 1
6p21.3
2.68E-05
NA
DEd
NA
DEd
COL5A2
collagen, type V, alpha 2
2q14-q32
3.88E-05
Page 11 of 19
Table 2: List of 35 cross-species conserved early HCC signature genes with qRT-PCR and independent human/rat early HCC validations overlaid (Continued)
BIRC3
baculoviral IAP repeat-containing 3
11q22
6.19E-05
NA
DEd
NS
NS
NS
DPP4
dipeptidylpeptidase 4 (CD26, adenosine deaminase
complexing protein 2)
2q24.3
0.00034
DEa
DEc
NS
DEd
NS
AGL*
amylo-1, 6-glucosidase, 4-alpha-glucanotransferase
1p21
0.000426
NA
DEc
DEc
DEc
NS
DEd
PDZK1IP1
PDZK1 interacting protein 1
SRPX
sushi-repeat-containing protein, X-linked
IL13RA1
interleukin 13 receptor, alpha 1
IGFBP3
insulin-like growth factor binding protein 3
PLN*
phospholamban
1 Genes
1p33
0.001118
NA
DEc
NS
NS
Xp21.1
0.001189
NA
DEc
DEc
DEd
NS
Xq24
0.004051
NA
DEc
NS
DEc
NS
7p13-p12
0.006904
DEa
DEc
DEc
DEc
DEc
6q22.1
0.028062
NA
NS
NS
NS
NS
Colak et al. Molecular Cancer 2010, 9:146
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with asterisk are also located in the chromosomal CNA regions.
NA indicates genes for which qRT-PCR data are not available.
aValidation was performed using whole blood of human early HCC patients (p < 0.05)
bValidation was performed on rat early HCC tissues (p < 0.05); five additional rat genes that are not listed in Table 2 are also confirmed (Figure 6).
cDifferentially expressed (p < 0.02 and fold change (FC) > 1.8); NS: Not significant
dDifferentially expressed: FC > 1.8, but not statistically significant (p > 0.05)
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Page 12 of 19
Figure 5 The interaction network analysis of 75 early HCC signature genes conserved across species and having genomic alterations. The
network analysis of 75 cross-species conserved signature genes with CN alterations indicated the importance of NF-κB, p38 MAPK, AP1 and JNK activation in early hepatoma formation.
DCN, MMP2, CD14, DCK, BIRC3, GJA1, LOX, SP100,
PROM1 and CREB1 were known to regulate tumorigenesis, neoplasia, apoptosis, growth, differentiation and proliferation. Some of the most significantly activated
canonical pathways included hepatic fibrosis/hepatic stellate cell activation (CD14, COL1A1, FGFR2, IGFBP3,
MMP2, and TGFBR1), and p38 MAPK signaling pathways (CREB1, PLA2G2A, TGFBR1). The network analysis
of early HCC signature genes indicated the activation of
ERK/MAPK, PI3K/AKT, and TGF-β signaling pathway,
and a potential critical regulatory role of MYC, ERbB-2,
HNF4A, and SMAD3 for early HCC (Figures 2, 3 and 4).
MAPKs are implicated in diverse cellular processes such
as cell survival, differentiation, adhesion, and proliferation [46]. The gene network analysis of differentially
expressed genes further confirmed the altered pathways.
Moreover, it also indicated the importance of NF-κB,
RAS and JNK activation in early hepatoma formation
(Figure 5 and Additional file 4).The role of MYC in various types of carcinogenesis has been extensively investi-
gated [47]. Most recently, JNK1 activation[48], and
increased expression of ErbB-2 were found to be associated with HCC [49]. Thus, our current findings are consistent with previously performed independent cancer
studies, including those for HCC. However, the novelty of
our approach is that using comparative and integrative
genomics, we provide evidence for the potential central
role of these genes in the earliest phase of liver malignant
transformation.
Our comparative genomics analysis resulted in a 35gene cross-species conserved signature for all types of
early HCCs. Over 70% of the conserved genes were associated with cancer according to the IPA knowledgebase,
including LGALS3BP[50,51], VIM[52,53], DCN[54,55],
IGFBP3[56], FGFR2[57], GJA1[58], SP100[59], DPP4[60],
PROM1[61], BIRC3[62], MMP2[63], and COL1A1[64,65]
(Table 2). Furthermore, using literature mining tools,
such as MILANO [66], we found that almost 90% of our
signature genes were reported to be cancer related.
Colak et al. Molecular Cancer 2010, 9:146
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Page 13 of 19
Figure 6 Confirmation of the microarray gene expression for six randomly selected significantly regulated genes in rat early HCC by realtime qRT-PCR. Ratio of expression (fold change) for each gene in (A) early HCC in young (DY) compared to normal (NY); (B) DY group to regenerated
(RY), (C) early HCC in old (DO) compared to normal (NO); (D) DO group to regenerated (RO). A significant correlation existed between the microarray
and realtime RT-PCR results (p < 0.001), thus demonstrating the reliability of our gene expression measurements. The fold changes were log2 transformed for both microarray data and real-time RT-PCR. Grey bars represent microarray hybridizations, and, and dark bars represent values from qRTPCR. The error bar represents standard deviation (SD) over four experiments. P-values for triplicate analyses were all < 0.05.
There are areas of genomic instability reported in many
cancers, including HCC, and some regions commonly
exhibit either deletion or increased gene dosage, leading
to changes in DNA copy number (CN) [9,26,29,30]. Integrating the gene expression with the CN data reveals the
chromosomal regions with concordantly altered genomic
and transcriptional status in tumors [24,32,67]. Hence,
focusing on differentially expressed genes with concomitant altered DNA copy number may identify novel early
HCC markers of malignant transformation and progression. The presence of altered DNA CN and LOH may
contribute to cancer formation [30,31,68]. Therefore, the
pattern of genomic modifications in a tumor represents a
structural fingerprint that may include the transcriptional
control mechanisms and locally impact gene expression
levels [31,32]. We identified that more than 50% of our
cross-species conserved early HCC signature genes were
found to be copy number dependent (Table 2).
We found significant expression of LGALS3BP (Lectin,
galactoside binding soluble 3 binding protein) and
COL1A1 located on Chromosome 17q. The LGALS3BP is
a 90-kD protein, designated serum protein 90 K that was
found at elevated concentrations in the serum of patients
with various types of breast, lung, colorectal, ovarian, and
endometrial cancer [50,51]. It is a secreted glycoprotein
that binds galectins, beta1-integrins, collagens, and
fibronectin, and has some relevance in cell-cell and cellextracellular matrix adhesion [69]. Another gene which
could be a potential biomarker for early HCC is dipeptidyl peptidase IV (DPP4). DPP4 is a serine protease, which
plays an important role in immune regulation, signal
transduction, and apoptosis. It has been shown that DPP4
may have a critical function in tumor progression in several human malignancies [60,70]. Matrix metalloproteinases (MMP) also are involved with early carcinogenic
events, tumor growth, tumor invasion and metastasis
[63,71,72]. Matrix metalloproteinases (MMPs) are zincdependent endopeptidases that cleave and degrade a
wide spectrum of extracellular matrix components, and
are involved with extracellular matrix remodeling during
the process of tumor invasion and metastasis [72]. Alterations in MMP expression and their endogenous inhibitor
(TIMP) may contribute to HCC metastasis [71-73].
Colak et al. Molecular Cancer 2010, 9:146
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Figure 7 Differential expression of a subset of genes was confirmed in whole blood of human early HCC subjects with qRTPCR. The up-regulation of expression of eight genes from Table 2 was
confirmed in blood of early HCC patients compared to normal controls
by using qRT-PCR. Values represent log2 of fold change in mRNAs in
early HCC relative to the healthy control subjects (in every case, p <
0.05, Student's t-test). The error bar represents standard deviation (SD)
over at least six experiments.
It is worth mentioning that, the gene "Probasin" (Pbsn),
was significantly up-regulated (fold change > 15 in both
old and young early rat HCCs). The high expression of
Pbsn in our rat model was also confirmed with realtime
RT-PCR. Pbsn is a member of the lipocalin family and has
not yet been associated with HCC in rats and has no
known human ortholog. However, it has been shown that
Pbsn is highly expressed in prostate and implicated in
both benign prostatic hyperplasia and prostate cancer
[74-76] and taste bud tumorigenesis in rats [74]. Also
since the promoter of this gene exhibits strong androgen
receptor-specific and tissue-specific regulation, Pbsn is
proposed to be a potential candidate for targeted therapies for advanced prostate cancer [77].
We have also found significant expression of lumican
(LUM) and decorin (DCN) in both early rat and human
HCCs. LUM and DCN are members of a small leucinerich proteoglycan (SLRP) family. Lumican has been
shown to participate in the maintenance of tissue homeostasis and modulation of cellular functions including cell
proliferation, migration, adhesion, and differentiation
[78]. Decorin has been reported to have a number of
functions including suppressing cancer cell growth and
metastasis andacting with extracellular matrix molecules
to influence cell adhesion and fibril stability [55]. DCN
acts as a natural inhibitor of TGF and is considered to be
a specific antagonist of EGFR [54]. In addition, the altered
expression of lumican and decorin has been associated
with various human cancers including breast, pancreatic,
lung, ovarian, melanoma, colorectal, osteosarcoma and
ductal adenocarcinoma [54,78-82].
Genes whose protein products are released into the
extracellular space would be ideal tumor markers for clin-
Page 14 of 19
ical applications, as it would be possible to detect these
proteins in patients' biological fluids rather than through
the use of invasive biopsies. Moreover, previous studies
have found that cells derived from peripheral blood could
be used to assess disease-associated gene signatures [8389]. In our study, we confirmed the high expression of
eight selected candidate biomarker genes (GJA1, VIM,
IGFBP3, COL1A1, SP100, MMP2, LGALS3BP, and DPP4)
by using realtime RT-PCR from the blood of early HCC
patients. These genes and other potential biomarker
genes identified through our integrated-comparative
genomics approach (listed in Table 2) and their encoded
proteins will be further studied in a large cohort of
patients to determine if they have a role in early HCC
pathology and if they could be novel early HCC biomarkers detectable in biological fluids.
Conclusions
In summary, to our knowledge, this is the first study to
examine HCC differentiated from regeneration in both
old and young rats, and coupled with a cross-species
comparative and integrative genomics approach to identify genes that could be potential biomarkers for early
human HCC. The results of our study include the depiction of refined and delineated biological pathways differentially modulated in HCC that is built around TP53, p38
MAPK, ERK/MAPK, PI3K/Akt, NF-κB, TGF-β, MYC,
and ERbB-2, including their target genes that were not
previously implicated with early HCC. Our cross-species
comparative and integrative genomics approach which
involved integration of multiple high dimensional independent datasets has led to potentially robust biomarkers
for the detection of early HCC. The signature genes that
we identified could be considered as "evolutionarily conserved cross-species biomarkers for early HCC with
genomic copy number alterations". Further studies are
needed to identify if any of the potential biomarkers identified in this study can be readily and reproducibly
detected in blood, urine or other bodily fluids. This could
then form the basis of a useful diagnostic test for the
detection of early HCC.
Methods
Animals
Male Sprague-Dawley rats were maintained at the King
Fahad National Centre for Children's Cancer and
Research Animal Facility. This facility is managed in
accordance with AALAS regulations. Ten young adult (5
months) and ten old adult (21 months) animals were subjected to partial hepatectomy. Actual survival rates
allowed for four animals in each group to be analyzed.
The re-growth of one lobe of the liver was completed
within one month, by which time the liver cells again
became quiescent, which was confirmed by histological
Colak et al. Molecular Cancer 2010, 9:146
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analysis. In parallel, separate animals were treated with
diethylnitrosoamine (200 mg/kg), which was injected
intraperitoneally to induce the formation of HCC. Once
the early HCC formation became apparent within 2-4
weeks, the rats were sacrificed. In the partial hepatectomized animals, one unaffected lobe and the regenerated
lobe of the liver were removed independently. In the carcinogenic treated animals the tumors were carefully dissected to avoid removing normal tissue. All tissues were
snap-frozen and stored at -80°C until required for RNA
isolation. Small pieces of tissue were removed for formalin fixation to be used for histological examination.
Human Subjects
Twenty blood samples were collected for this study (10
early HCC and 10 from healthy controls). Histopathological classification of HCC and clinical staging of early
HCC were performed according to International Working Party [90] as previously described [8]. Patients diagnosed with the early HCC and healthy controls were
recruited under an institutional review board-approved
project (RAC# 2060040); all subjects provided written,
informed consent before entry in the study. A total of 4
ml (in two separate PaxGene tubes) of whole blood samples were collected for each individual according to manufacturer's guidelines (QIAGEN Inc., Valencia, CA,
USA). The total RNA isolation was performed using PreAnalytiX - PAXgene Blood RNA System (QIAGEN Inc.)
by strictly following the manual and protocols provided
with the kit-system.
Microarray Hybridization
Total RNA was isolated according to standard protocols.
Quality Control of RNA was done using Bioanalyzer 2100
RNA 6000 NanoAssay and RNA above RIN = 8 was
included to the study (Agilent Technologies, Santa Clara,
USA). Rat Genome Survey Microarray (Applied Biosystems, Foster City, CA, USA) was utilized for microarray
studies. cDNA synthesis, cRNA and labeling, chemiluminescence detection, image acquisition and analysis were
performed following the manufacturer's protocols, guidelines and recommendations.
Microarray Data Analysis
Images were auto-gridded and the chemiluminescent signals were quantified, then background subtracted using
the Applied Biosystems 1700 Chemiluminescent
Microarray Analyzer software v 1.1. For transcriptome
analysis, detection thresholds were used following the
manufacturer's recommendations. Detection threshold
was set as S/N > 3 and quality flag < 5000. The microarray
data were analyzed from 24 samples (2 samples were
excluded for quality reasons). The open source R software
package http://www.r-project.org and tools from the BioConductor project were used for normalization and
Page 15 of 19
determination of differentially expressed genes [91]. Twofactor Analysis of Variance (ANOVA) was performed to
include both "treatment" (HCC, regenerated and normal,
which will be referred to as treatment in the remainder of
the manuscript), as well as age (old and young) factor
together with feature selection algorithm (also known as
template matching(TMA)) [28] to look for treatment as
well as age specific variation. Significantly modulated
genes specific to HCC were defined as those with
ANOVA (treatment) p- value < 0.01, and TMA p-value <
0.01. Additionally, samples were stratified as young and
old cohorts, and HCC specific genes identified using oneway ANOVA in each age group separately. When comparing HCC group with regenerated and normal controls
to identify the differentially expressed genes specific to
the HCC, we used a combination of three criteria. We
considered genes that are "present" in at least half of the
samples in either group. HCC specific genes were defined
as those with in absolute fold change > 1.8 and p-value <
0.02. These observations are consistent with the study of
Guo et. al., in which gene lists ranked by fold change and
filtered with non-stringent statistically significant tests
were more reproducible across platforms than those generated through other analytical procedures [43]. A validation datasets were generated from three independent
human HCC studies by Chiang et. al. [33] (GSE9843),
which was composed of 91 HCV-related HCC tumor
samples, of which 65 were very early or early stage disease
(we used in our re-analysis only very early and early HCC
datasets) and Boyault et. al. [34] (E-TABM-36) which
consisted of 57 human HCCs and five samples of pooled
non-tumorus tissues, and from Liao et. al. [35] (GSE
6222) consisting of various stages of HCC (we used
expression data for only the early stage of the disease).
Furthermore, we compared our results with the results
from two independent studies [19,38] with the DENinduced HCC in rats. The raw data was analyzed by using
dChip[92] and open source R/Bioconductor packages.
The dChip outlier detection algorithm was used to identify outlier arrays, and probes "present" in at least 50% of
the samples in either group were filtered. The data was
normalized by the GC Robust Multi-array Average (GCRMA) algorithm [93,94]. Unpaired t-tests were performed to determine significant differences in gene
expression levels between patients and normal controls.
The Hierarchical clustering using Pearson's correlation
with average linkage clustering was performed by MeV
4.0 [95].
Information about genes participating in known biological process and pathways were derived by using
DAVID Bioinformatics Resources[96], Expression Analysis Systematic Explorer (EASE)[97], and PANTHER (Protein ANalysis THrough Evolutionary Relationships)
Classification Systems [98]. For each molecular function,
Colak et al. Molecular Cancer 2010, 9:146
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biological process or pathway term, PANTHER calculates
the number of genes identified in that category in both a
list of differentially regulated genes and a reference list
containing all the probe sets present on the chip and
compares these results using the binomial test to determine if there are more genes than expected in the differentially regulated list [99]. Over-representation is defined
by p < 0.05. Statistical analyses were performed with the
MATLAB software packages (Mathworks, Natick, MA,
USA), R and Bioconductor and PARTEK Genomics Suite
(Partek Inc, St. Lois, MO, USA).
Functional Pathway and Network Analysis
Functional pathway, gene ontology and network analyses
were executed using Ingenuity Pathways Analysis (IPA)
6.3 (Ingenuity Systems, Mountain View, CA). The differentially expressed signature gene lists for hepatoma and
regeneration in different age groups were mapped to its
corresponding gene object in the Ingenuity pathway
knowledge base. These so-called focus genes were then
used as a starting point for generating biological networks. A score was assigned to each network in the dataset to estimate the relevance of the network to the
uploaded gene list. This score reflects the negative logarithm of the P that indicates the likelihood of the focus
genes in a network being found together due to random
chance. Using a 99% confidence level, scores of ≥2 were
considered significant. Significances for biological functions or pathways in the signature genes for such functions or pathways compared with the ABI Rat Genome
Survey Microarray as a reference set. A right-tailed
Fisher's exact test was used to calculate a p-value determining the probability that the biological function (or
pathway) assigned to that data set is explained by chance
alone.
Cross-Species Comparative and Integrative Genomic
Analysis
Human early HCC datasets from two independent studies by Mas et. al. [27] using Affymetrix HG-U133A 2.0
array, and Wumbach et. al. [8] using Affymetrix HGU133 Plus 2.0 were re-analyzed. The raw data were analyzed using R/Bioconductor packages and Partek
Genomics Suite (Partek Inc.). The data were normalized
by the GC Robust Multi-array Average (GC-RMA) algorithm. Unpaired t-tests were performed to determine significant differences in gene expression levels between
patients and normal controls. The cross mapping of
Applied Biosystems Rat Genome Survey microarray
probes were mapped to human orthologs through
"AB1700 rat annotation spreadsheet" designed by Applied
Biosystems on the basis of sequence identity. The transcripts present on both platforms (AB1700 and Affymetrix) were identified using Resourcerer [100]. Genes
Page 16 of 19
within copy number altered regions based on three independent genome CNA studies of human HCC [7,9,10]
were determined using NCBI MapViewer http://
www.ncbi.nlm.nih.gov/mapview, and integrated those
with the gene expression profiling data (Figure 1).
Realtime RT-PCR Experiments
Confirmatory realtime RT-PCR experiments were performed using the ABI 7500 Sequence Detection System
(Applied Biosystems). 50 ng total RNA procured from the
same microarray study samples were transcribed into
cDNA using a Sensicript Kit (QIAGEN Inc., Valencia,
CA, USA) under the following conditions: 25°C for 10
min, 42°C for 2 hrs, and 70°C for 15 min in a total volume
of 20 μl. Six differentially expressed rat genes (Pbsn,
Cdh13, Lum, Nid2, Dcn, Slc22a5) and eight human genes
(GJA1, VIM, IGFBP3, COL1A1, SP100, MMP2,
LGALS3BP, and DPP4) were selected and primers
designed using Primer3 software. For the human samples, blood total RNA was utilized. After primer optimization, realtime PCR experiments were performed with 6
μl cDNA using Quantitech SyBr Green Kit (QIAGEN),
employing GAPDH as the endogenous control gene. All
reactions were conducted in triplicates and the data was
analyzed using the delta delta CT method [101,102].
Additional material
Additional file 1 Selected HCC specific genes, conserved across both
age groups (old and young), and significantly modulated with respect
to regenerated and normal liver.
Additional file 2 Comparison of expression profiles of HCC and regeneration within the same age group. (A, D) Heatmap of significantly dysregulated genes due to different treatment types in young and old,
respectively. (B, E) Hierarchical clustering of samples separated based on
treatment type in young and old, respectively. The gene expression clustering distance between the HCC group and other two groups (regenerated
and normal) was the greatest in both age groups (C, F) Principle component analysis (PCA) which contained almost 76% of the variance in the data
matrix clearly separated samples based on the treatment type in young
and old, respectively.
Additional file 3 Heatmap and gene interaction networks of HCC specific genes in the old age group. (A) Venn diagram characterizing differential gene expression between and specific to different treatment types
(the HCC, the regenerated and the normal). The number of HCC specific
genes, 100, is circled in black. (B) Heatmap of HCC specific genes exclusively
dysregulated (up/down regulated) in the HCC group only. (C-E) Functional
network analysis of HCC specific genes. Top three scoring gene interaction
networks (with highest relevance scores) are shown. Nodes represent
genes, with their shape representing the functional class of the gene product, and edges indicate biological relationship between the nodes (see legend in Figure 2). (F) Top network functions associated with three networks
shown. An IPA score of three indicates that there is 1/1000 (score = -log (pvalue)) chance that the focus genes are assigned to a network randomly.
Additional file 4 The gene interaction network analysis early HCC signature genes that are conserved in rat early HCC and in either of multiple human early HCCs (A, B) The top two scoring gene interaction
networks of 154 cross-species conserved signature genes indicated the
importance of NF-κB, RAS and JNK activation in early hepatoma formation.
Nodes represent genes, with their shape representing the functional class
of the gene product, and edges indicate biological relationship between
the nodes (see legend in Figure 5).
Colak et al. Molecular Cancer 2010, 9:146
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Additional file 5 The unsupervised Principle Components Analysis
(PCA) was performed using our 35-gene signature to cluster samples
from independent validation dataset of Chiang et. al. Our signature
gene list was sufficient to separate individuals in Chiang et al.'s study as
either early HCC patients or normal controls.
Competing interests
The authors declare that they have no competing interests.
Authors' contributions
DC and NK conceived the research problem and designed the methodology.
MAC developed the rat model. MG provided suggestions for the rat model. NK
and AA carried out microarray experiments. DC collected and processed the
data, performed statistical and bioinformatics analyses, and drafted the manuscript together with NK and BHP. JQ and MMS provided suggestions for
method design and commented on the manuscript. MAC, PTO, BHP, and AAQ
provided general interpretation of results. All authors read and approved the
final manuscript.
Acknowledgements
We thank Maqbool Ahmad for providing help in RNA isolation, and Bedri Karakas for critically reading the manuscript. We would like to thank to King Faisal
Specialist Hospital and Research Center for the financial support.
Author Details
1Department of Biostatistics, Epidemiology and Scientific Computing, King
Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia,
2Department of Medical Biochemistry/Obesity Research Center, King Saud
University, Riyadh, Saudi Arabia, 3Department of Genetics, King Faisal Specialist
Hospital and Research Centre, Riyadh, Saudi Arabia, 4Department of Biological
and Medical Research, King Faisal Specialist Hospital and Research Centre,
Riyadh, Saudi Arabia, 5Liver Research Center, College of Medicine, King Saud
University, Riyadh, Saudi Arabia, 6Immorgene Concepts Ltd, Stockton-on-Tees,
UK, 7Duzen Laboratories, Istanbul, Turkey, 8Department of Biostatistics and
Computational Biology; Dana-Farber Cancer Institute, Boston, MA, USA and
9The Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins,
Department of Oncology, Johns Hopkins University School of Medicine,
Baltimore, MD, USA
Received: 7 December 2009 Accepted: 12 June 2010
Published: 12 June 2010
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doi: 10.1186/1476-4598-9-146
Cite this article as: Colak et al., Integrative and comparative genomics analysis of early hepatocellular carcinoma differentiated from liver regeneration in
young and old Molecular Cancer 2010, 9:146